{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "year=2019\n",
    "month=5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import sys\n",
    "sys.path.append('../py')\n",
    "import db\n",
    "import weighted\n",
    "import inspect\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as stats\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_original=pd.read_sql(sql=f\"select * from _{year}{month:02} where monthly_salary>0 and monthly_salary<80000 and YEAR(publish_date)={year} and MONTH(publish_date)={month}\", con=db.get_conn())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(86722, 94)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_original.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "error_job_ids=['104660258','104142922','108434795','101357291','106253516','110368302','111391233','108665401','109277048'\n",
    "                  ,'73857191','108584955','102824950','102824949','111391233','110884556']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=data_original[~data_original.job_id.isin(error_job_ids)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(86720, 94)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "del data['publish_date']\n",
    "del data['published_on_weekend']\n",
    "del data['title']\n",
    "del data['company_title']\n",
    "del data['company_description']\n",
    "del data['job_description']\n",
    "del data['job_id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_sub_stats_by_prefix(data, prefix):\n",
    "    \n",
    "    features = [feature for feature in data.columns if feature.startswith(prefix)]\n",
    "    salary_mean=[]\n",
    "    salary_median=[]\n",
    "    salary_95_min=[]\n",
    "    salary_95_max=[]\n",
    "    count=[]\n",
    "    \n",
    "    features_out=[]\n",
    "    for feature in features:\n",
    "        #print(feature)\n",
    "        idata=data[data[feature]==1]\n",
    "        headcount=idata.headcount.sum()\n",
    "        values = idata.monthly_salary.values\n",
    "        weights = idata.headcount.values\n",
    "        #print(str(headcount))\n",
    "        if headcount==0:\n",
    "            continue\n",
    "        \n",
    "        salary_mean.append(weighted.weighted_mean(values, weights))\n",
    "        q = weighted.weighted_quantile(values,[0.025,0.5,0.975],weights)\n",
    "        salary_median.append(q[1])\n",
    "        salary_95_min.append(q[0])\n",
    "        salary_95_max.append(q[2])\n",
    "        count.append(idata.headcount.sum())\n",
    "        features_out.append(feature)\n",
    "    sub_data=pd.DataFrame()\n",
    "    sub_data['rank']=range(0,len(features_out))\n",
    "    sub_data[prefix]=[f.replace(prefix,'') for f in features_out]\n",
    "    sub_data['salary_mean']=salary_mean\n",
    "    sub_data['salary_median']=salary_median\n",
    "    sub_data['salary_95_min']=salary_95_min\n",
    "    sub_data['salary_95_max']=salary_95_max\n",
    "    sub_data['head_count']=count\n",
    "    sub_data['percentage']=count/np.sum(count)\n",
    "    sub_data=sub_data.sort_values(by='salary_mean', ascending=False)\n",
    "    sub_data['rank']=range(1,len(features_out)+1)\n",
    "    #sub_data=sub_data.reset_index()\n",
    "    return sub_data\n",
    "\n",
    "def apply_style(sub_data):\n",
    "    return sub_data.style.hide_index().format(\n",
    "        {\"percentage\":\"{:.2%}\",\"salary_mean\":\"{:.0f}\",\"salary_median\":\"{:.0f}\",\"salary_95_min\":\"{:.0f}\",\"salary_95_max\":\"{:.0f}\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_pl=get_sub_stats_by_prefix(data,'pl_')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_63da780a_8454_11e9_ba22_701ce71031ef\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"col_heading level0 col0\" >rank</th> \n",
       "        <th class=\"col_heading level0 col1\" >pl_</th> \n",
       "        <th class=\"col_heading level0 col2\" >salary_mean</th> \n",
       "        <th class=\"col_heading level0 col3\" >salary_median</th> \n",
       "        <th class=\"col_heading level0 col4\" >salary_95_min</th> \n",
       "        <th class=\"col_heading level0 col5\" >salary_95_max</th> \n",
       "        <th class=\"col_heading level0 col6\" >head_count</th> \n",
       "        <th class=\"col_heading level0 col7\" >percentage</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow0_col0\" class=\"data row0 col0\" >1</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow0_col1\" class=\"data row0 col1\" >haskell</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow0_col2\" class=\"data row0 col2\" >24255</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow0_col3\" class=\"data row0 col3\" >20278</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow0_col4\" class=\"data row0 col4\" >7938</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow0_col5\" class=\"data row0 col5\" >45000</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow0_col6\" class=\"data row0 col6\" >47</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow0_col7\" class=\"data row0 col7\" >0.01%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow1_col0\" class=\"data row1 col0\" >2</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow1_col1\" class=\"data row1 col1\" >julia</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow1_col2\" class=\"data row1 col2\" >21562</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow1_col3\" class=\"data row1 col3\" >21500</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow1_col4\" class=\"data row1 col4\" >14000</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow1_col5\" class=\"data row1 col5\" >37500</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow1_col6\" class=\"data row1 col6\" >8</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow1_col7\" class=\"data row1 col7\" >0.00%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow2_col0\" class=\"data row2 col0\" >3</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow2_col1\" class=\"data row2 col1\" >rust</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow2_col2\" class=\"data row2 col2\" >18937</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow2_col3\" class=\"data row2 col3\" >17500</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow2_col4\" class=\"data row2 col4\" >5016</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow2_col5\" class=\"data row2 col5\" >57083</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow2_col6\" class=\"data row2 col6\" >295</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow2_col7\" class=\"data row2 col7\" >0.08%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow3_col0\" class=\"data row3 col0\" >4</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow3_col1\" class=\"data row3 col1\" >python</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow3_col2\" class=\"data row3 col2\" >17810</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow3_col3\" class=\"data row3 col3\" >15000</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow3_col4\" class=\"data row3 col4\" >3750</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow3_col5\" class=\"data row3 col5\" >43958</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow3_col6\" class=\"data row3 col6\" >27930</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow3_col7\" class=\"data row3 col7\" >7.30%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow4_col0\" class=\"data row4 col0\" >5</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow4_col1\" class=\"data row4 col1\" >go</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow4_col2\" class=\"data row4 col2\" >17526</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow4_col3\" class=\"data row4 col3\" >15000</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow4_col4\" class=\"data row4 col4\" >5250</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow4_col5\" class=\"data row4 col5\" >40000</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow4_col6\" class=\"data row4 col6\" >26704</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow4_col7\" class=\"data row4 col7\" >6.98%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow5_col0\" class=\"data row5 col0\" >6</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow5_col1\" class=\"data row5 col1\" >matlab</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow5_col2\" class=\"data row5 col2\" >17460</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow5_col3\" class=\"data row5 col3\" >16666</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow5_col4\" class=\"data row5 col4\" >5201</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow5_col5\" class=\"data row5 col5\" >37500</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow5_col6\" class=\"data row5 col6\" >5612</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow5_col7\" class=\"data row5 col7\" >1.47%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow6_col0\" class=\"data row6 col0\" >7</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow6_col1\" class=\"data row6 col1\" >perl</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow6_col2\" class=\"data row6 col2\" >17006</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow6_col3\" class=\"data row6 col3\" >15000</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow6_col4\" class=\"data row6 col4\" >3750</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow6_col5\" class=\"data row6 col5\" >40000</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow6_col6\" class=\"data row6 col6\" >2397</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow6_col7\" class=\"data row6 col7\" >0.63%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow7_col0\" class=\"data row7 col0\" >8</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow7_col1\" class=\"data row7 col1\" >lua</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow7_col2\" class=\"data row7 col2\" >16347</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow7_col3\" class=\"data row7 col3\" >15000</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow7_col4\" class=\"data row7 col4\" >3750</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow7_col5\" class=\"data row7 col5\" >37500</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow7_col6\" class=\"data row7 col6\" >2727</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow7_col7\" class=\"data row7 col7\" >0.71%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow8_col0\" class=\"data row8 col0\" >9</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow8_col1\" class=\"data row8 col1\" >ruby</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow8_col2\" class=\"data row8 col2\" >16161</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow8_col3\" class=\"data row8 col3\" >15000</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow8_col4\" class=\"data row8 col4\" >3000</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow8_col5\" class=\"data row8 col5\" >34091</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow8_col6\" class=\"data row8 col6\" >1140</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow8_col7\" class=\"data row8 col7\" >0.30%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow9_col0\" class=\"data row9 col0\" >10</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow9_col1\" class=\"data row9 col1\" >kotlin</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow9_col2\" class=\"data row9 col2\" >15213</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow9_col3\" class=\"data row9 col3\" >12500</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow9_col4\" class=\"data row9 col4\" >5419</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow9_col5\" class=\"data row9 col5\" >37500</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow9_col6\" class=\"data row9 col6\" >789</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow9_col7\" class=\"data row9 col7\" >0.21%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow10_col0\" class=\"data row10 col0\" >11</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow10_col1\" class=\"data row10 col1\" >cpp</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow10_col2\" class=\"data row10 col2\" >15109</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow10_col3\" class=\"data row10 col3\" >12500</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow10_col4\" class=\"data row10 col4\" >3750</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow10_col5\" class=\"data row10 col5\" >37500</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow10_col6\" class=\"data row10 col6\" >61225</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow10_col7\" class=\"data row10 col7\" >16.00%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow11_col0\" class=\"data row11 col0\" >12</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow11_col1\" class=\"data row11 col1\" >swift</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow11_col2\" class=\"data row11 col2\" >14435</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow11_col3\" class=\"data row11 col3\" >12500</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow11_col4\" class=\"data row11 col4\" >4800</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow11_col5\" class=\"data row11 col5\" >35000</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow11_col6\" class=\"data row11 col6\" >2924</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow11_col7\" class=\"data row11 col7\" >0.76%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow12_col0\" class=\"data row12 col0\" >13</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow12_col1\" class=\"data row12 col1\" >typescript</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow12_col2\" class=\"data row12 col2\" >13840</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow12_col3\" class=\"data row12 col3\" >12500</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow12_col4\" class=\"data row12 col4\" >5342</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow12_col5\" class=\"data row12 col5\" >30000</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow12_col6\" class=\"data row12 col6\" >984</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow12_col7\" class=\"data row12 col7\" >0.26%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow13_col0\" class=\"data row13 col0\" >14</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow13_col1\" class=\"data row13 col1\" >objective_c</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow13_col2\" class=\"data row13 col2\" >13202</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow13_col3\" class=\"data row13 col3\" >12500</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow13_col4\" class=\"data row13 col4\" >5821</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow13_col5\" class=\"data row13 col5\" >25000</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow13_col6\" class=\"data row13 col6\" >384</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow13_col7\" class=\"data row13 col7\" >0.10%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow14_col0\" class=\"data row14 col0\" >15</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow14_col1\" class=\"data row14 col1\" >java</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow14_col2\" class=\"data row14 col2\" >13066</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow14_col3\" class=\"data row14 col3\" >12500</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow14_col4\" class=\"data row14 col4\" >3750</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow14_col5\" class=\"data row14 col5\" >30000</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow14_col6\" class=\"data row14 col6\" >128804</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow14_col7\" class=\"data row14 col7\" >33.66%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow15_col0\" class=\"data row15 col0\" >16</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow15_col1\" class=\"data row15 col1\" >php</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow15_col2\" class=\"data row15 col2\" >12782</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow15_col3\" class=\"data row15 col3\" >11000</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow15_col4\" class=\"data row15 col4\" >3750</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow15_col5\" class=\"data row15 col5\" >35000</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow15_col6\" class=\"data row15 col6\" >18178</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow15_col7\" class=\"data row15 col7\" >4.75%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow16_col0\" class=\"data row16 col0\" >17</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow16_col1\" class=\"data row16 col1\" >javascript</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow16_col2\" class=\"data row16 col2\" >11423</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow16_col3\" class=\"data row16 col3\" >10500</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow16_col4\" class=\"data row16 col4\" >3750</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow16_col5\" class=\"data row16 col5\" >24017</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow16_col6\" class=\"data row16 col6\" >50830</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow16_col7\" class=\"data row16 col7\" >13.28%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow17_col0\" class=\"data row17 col0\" >18</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow17_col1\" class=\"data row17 col1\" >visual_basic</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow17_col2\" class=\"data row17 col2\" >11286</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow17_col3\" class=\"data row17 col3\" >9000</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow17_col4\" class=\"data row17 col4\" >4500</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow17_col5\" class=\"data row17 col5\" >30000</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow17_col6\" class=\"data row17 col6\" >71</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow17_col7\" class=\"data row17 col7\" >0.02%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow18_col0\" class=\"data row18 col0\" >19</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow18_col1\" class=\"data row18 col1\" >c_sharp</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow18_col2\" class=\"data row18 col2\" >10963</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow18_col3\" class=\"data row18 col3\" >10000</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow18_col4\" class=\"data row18 col4\" >3750</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow18_col5\" class=\"data row18 col5\" >25000</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow18_col6\" class=\"data row18 col6\" >49900</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow18_col7\" class=\"data row18 col7\" >13.04%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow19_col0\" class=\"data row19 col0\" >20</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow19_col1\" class=\"data row19 col1\" >vba</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow19_col2\" class=\"data row19 col2\" >10954</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow19_col3\" class=\"data row19 col3\" >10416</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow19_col4\" class=\"data row19 col4\" >4500</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow19_col5\" class=\"data row19 col5\" >24090</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow19_col6\" class=\"data row19 col6\" >371</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow19_col7\" class=\"data row19 col7\" >0.10%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow20_col0\" class=\"data row20 col0\" >21</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow20_col1\" class=\"data row20 col1\" >delphi</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow20_col2\" class=\"data row20 col2\" >10699</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow20_col3\" class=\"data row20 col3\" >9500</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow20_col4\" class=\"data row20 col4\" >3750</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow20_col5\" class=\"data row20 col5\" >23500</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow20_col6\" class=\"data row20 col6\" >1345</td> \n",
       "        <td id=\"T_63da780a_8454_11e9_ba22_701ce71031efrow20_col7\" class=\"data row20 col7\" >0.35%</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2563469c6a0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apply_style(data_pl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_64defbde_8454_11e9_8091_701ce71031ef\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"col_heading level0 col0\" >rank</th> \n",
       "        <th class=\"col_heading level0 col1\" >pl_</th> \n",
       "        <th class=\"col_heading level0 col2\" >percentage</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <td id=\"T_64defbde_8454_11e9_8091_701ce71031efrow0_col0\" class=\"data row0 col0\" >1</td> \n",
       "        <td id=\"T_64defbde_8454_11e9_8091_701ce71031efrow0_col1\" class=\"data row0 col1\" >java</td> \n",
       "        <td id=\"T_64defbde_8454_11e9_8091_701ce71031efrow0_col2\" class=\"data row0 col2\" >33.66%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_64defbde_8454_11e9_8091_701ce71031efrow1_col0\" class=\"data row1 col0\" >2</td> \n",
       "        <td id=\"T_64defbde_8454_11e9_8091_701ce71031efrow1_col1\" class=\"data row1 col1\" >cpp</td> \n",
       "        <td id=\"T_64defbde_8454_11e9_8091_701ce71031efrow1_col2\" class=\"data row1 col2\" >16.00%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_64defbde_8454_11e9_8091_701ce71031efrow2_col0\" class=\"data row2 col0\" >3</td> \n",
       "        <td id=\"T_64defbde_8454_11e9_8091_701ce71031efrow2_col1\" class=\"data row2 col1\" >javascript</td> \n",
       "        <td id=\"T_64defbde_8454_11e9_8091_701ce71031efrow2_col2\" class=\"data row2 col2\" >13.28%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_64defbde_8454_11e9_8091_701ce71031efrow3_col0\" class=\"data row3 col0\" >4</td> \n",
       "        <td id=\"T_64defbde_8454_11e9_8091_701ce71031efrow3_col1\" class=\"data row3 col1\" >c_sharp</td> \n",
       "        <td id=\"T_64defbde_8454_11e9_8091_701ce71031efrow3_col2\" class=\"data row3 col2\" >13.04%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_64defbde_8454_11e9_8091_701ce71031efrow4_col0\" class=\"data row4 col0\" >5</td> \n",
       "        <td id=\"T_64defbde_8454_11e9_8091_701ce71031efrow4_col1\" class=\"data row4 col1\" >python</td> \n",
       "        <td id=\"T_64defbde_8454_11e9_8091_701ce71031efrow4_col2\" class=\"data row4 col2\" >7.30%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_64defbde_8454_11e9_8091_701ce71031efrow5_col0\" class=\"data row5 col0\" >6</td> \n",
       "        <td id=\"T_64defbde_8454_11e9_8091_701ce71031efrow5_col1\" class=\"data row5 col1\" >go</td> \n",
       "        <td id=\"T_64defbde_8454_11e9_8091_701ce71031efrow5_col2\" class=\"data row5 col2\" >6.98%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_64defbde_8454_11e9_8091_701ce71031efrow6_col0\" class=\"data row6 col0\" >7</td> \n",
       "        <td id=\"T_64defbde_8454_11e9_8091_701ce71031efrow6_col1\" class=\"data row6 col1\" >php</td> \n",
       "        <td id=\"T_64defbde_8454_11e9_8091_701ce71031efrow6_col2\" class=\"data row6 col2\" >4.75%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_64defbde_8454_11e9_8091_701ce71031efrow7_col0\" class=\"data row7 col0\" >8</td> \n",
       "        <td id=\"T_64defbde_8454_11e9_8091_701ce71031efrow7_col1\" class=\"data row7 col1\" >matlab</td> \n",
       "        <td id=\"T_64defbde_8454_11e9_8091_701ce71031efrow7_col2\" class=\"data row7 col2\" >1.47%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_64defbde_8454_11e9_8091_701ce71031efrow8_col0\" class=\"data row8 col0\" >9</td> \n",
       "        <td id=\"T_64defbde_8454_11e9_8091_701ce71031efrow8_col1\" class=\"data row8 col1\" >swift</td> \n",
       "        <td id=\"T_64defbde_8454_11e9_8091_701ce71031efrow8_col2\" class=\"data row8 col2\" >0.76%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_64defbde_8454_11e9_8091_701ce71031efrow9_col0\" class=\"data row9 col0\" >10</td> \n",
       "        <td id=\"T_64defbde_8454_11e9_8091_701ce71031efrow9_col1\" class=\"data row9 col1\" >lua</td> \n",
       "        <td id=\"T_64defbde_8454_11e9_8091_701ce71031efrow9_col2\" class=\"data row9 col2\" >0.71%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_64defbde_8454_11e9_8091_701ce71031efrow10_col0\" class=\"data row10 col0\" >11</td> \n",
       "        <td id=\"T_64defbde_8454_11e9_8091_701ce71031efrow10_col1\" class=\"data row10 col1\" >perl</td> \n",
       "        <td id=\"T_64defbde_8454_11e9_8091_701ce71031efrow10_col2\" class=\"data row10 col2\" >0.63%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_64defbde_8454_11e9_8091_701ce71031efrow11_col0\" class=\"data row11 col0\" >12</td> \n",
       "        <td id=\"T_64defbde_8454_11e9_8091_701ce71031efrow11_col1\" class=\"data row11 col1\" >delphi</td> \n",
       "        <td id=\"T_64defbde_8454_11e9_8091_701ce71031efrow11_col2\" class=\"data row11 col2\" >0.35%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_64defbde_8454_11e9_8091_701ce71031efrow12_col0\" class=\"data row12 col0\" >13</td> \n",
       "        <td id=\"T_64defbde_8454_11e9_8091_701ce71031efrow12_col1\" class=\"data row12 col1\" >ruby</td> \n",
       "        <td id=\"T_64defbde_8454_11e9_8091_701ce71031efrow12_col2\" class=\"data row12 col2\" >0.30%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_64defbde_8454_11e9_8091_701ce71031efrow13_col0\" class=\"data row13 col0\" >14</td> \n",
       "        <td id=\"T_64defbde_8454_11e9_8091_701ce71031efrow13_col1\" class=\"data row13 col1\" >typescript</td> \n",
       "        <td id=\"T_64defbde_8454_11e9_8091_701ce71031efrow13_col2\" class=\"data row13 col2\" >0.26%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_64defbde_8454_11e9_8091_701ce71031efrow14_col0\" class=\"data row14 col0\" >15</td> \n",
       "        <td id=\"T_64defbde_8454_11e9_8091_701ce71031efrow14_col1\" class=\"data row14 col1\" >kotlin</td> \n",
       "        <td id=\"T_64defbde_8454_11e9_8091_701ce71031efrow14_col2\" class=\"data row14 col2\" >0.21%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_64defbde_8454_11e9_8091_701ce71031efrow15_col0\" class=\"data row15 col0\" >16</td> \n",
       "        <td id=\"T_64defbde_8454_11e9_8091_701ce71031efrow15_col1\" class=\"data row15 col1\" >objective_c</td> \n",
       "        <td id=\"T_64defbde_8454_11e9_8091_701ce71031efrow15_col2\" class=\"data row15 col2\" >0.10%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_64defbde_8454_11e9_8091_701ce71031efrow16_col0\" class=\"data row16 col0\" >17</td> \n",
       "        <td id=\"T_64defbde_8454_11e9_8091_701ce71031efrow16_col1\" class=\"data row16 col1\" >vba</td> \n",
       "        <td id=\"T_64defbde_8454_11e9_8091_701ce71031efrow16_col2\" class=\"data row16 col2\" >0.10%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_64defbde_8454_11e9_8091_701ce71031efrow17_col0\" class=\"data row17 col0\" >18</td> \n",
       "        <td id=\"T_64defbde_8454_11e9_8091_701ce71031efrow17_col1\" class=\"data row17 col1\" >rust</td> \n",
       "        <td id=\"T_64defbde_8454_11e9_8091_701ce71031efrow17_col2\" class=\"data row17 col2\" >0.08%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_64defbde_8454_11e9_8091_701ce71031efrow18_col0\" class=\"data row18 col0\" >19</td> \n",
       "        <td id=\"T_64defbde_8454_11e9_8091_701ce71031efrow18_col1\" class=\"data row18 col1\" >visual_basic</td> \n",
       "        <td id=\"T_64defbde_8454_11e9_8091_701ce71031efrow18_col2\" class=\"data row18 col2\" >0.02%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_64defbde_8454_11e9_8091_701ce71031efrow19_col0\" class=\"data row19 col0\" >20</td> \n",
       "        <td id=\"T_64defbde_8454_11e9_8091_701ce71031efrow19_col1\" class=\"data row19 col1\" >haskell</td> \n",
       "        <td id=\"T_64defbde_8454_11e9_8091_701ce71031efrow19_col2\" class=\"data row19 col2\" >0.01%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_64defbde_8454_11e9_8091_701ce71031efrow20_col0\" class=\"data row20 col0\" >21</td> \n",
       "        <td id=\"T_64defbde_8454_11e9_8091_701ce71031efrow20_col1\" class=\"data row20 col1\" >julia</td> \n",
       "        <td id=\"T_64defbde_8454_11e9_8091_701ce71031efrow20_col2\" class=\"data row20 col2\" >0.00%</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2563b618f98>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pl=data_pl[['pl_','percentage']].sort_values(by='percentage', ascending=False)\n",
    "data_pl['rank']=range(1,22)\n",
    "data_pl=data_pl[['rank','pl_','percentage']]\n",
    "#data_pl.style.format({\"percentage\":\"{:.2%}\"})\n",
    "data_pl.style.hide_index().format({\"percentage\":\"{:.2%}\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_pl=data_pl.sort_values(by='percentage')\n",
    "\n",
    "# Pie chart, where the slices will be ordered and plotted counter-clockwise:\n",
    "labels = data_pl['pl_']\n",
    "sizes = data_pl['percentage']\n",
    "#explode = (0, 0.1, 0, 0)  # only \"explode\" the 2nd slice (i.e. 'Hogs')\n",
    "\n",
    "fig1, ax1 = plt.subplots(figsize=(15, 9))\n",
    "ax1.pie(sizes, labels=labels, autopct='%1.1f%%',\n",
    "        shadow=True, startangle=90)\n",
    "ax1.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "plt.title(\"Programming Languages in China, March 2019\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Word Cloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from wordcloud import WordCloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "wc=WordCloud()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "wc_dict = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "for index, row in data_pl.iterrows():\n",
    "    wc_dict[row['pl_']]=row['percentage']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "wc=wc.generate_from_frequencies(wc_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,5))\n",
    "plt.imshow(wc, interpolation=\"bilinear\")\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
